SCTK Cell QC Report
May 09, 2021
1 Introduction
Comprehensive quality control (QC) of single-cell RNA-seq data was performed with the singleCellTK package. This report contains information about each QC tool and visualization of the QC metrics for each sample. For more information on running this pipeline and performing quality control, see the documentation. If you use the singleCellTK package for quality control, please include a reference in your publication.
2 General quality control metrics
SingleCellTK utilizes the scater package to compute cell-level QC metrics. The wrapper function runPerCellQC can be used to separately compute QC metrics on its own. The wrapper function plotRunPerCellQCResults can be used to plot the general QC outputs. The QC outputs are sum, detected, and percent_top_X. sum contains the total number of counts for each cell. detected contains the total number of features for each cell. percent_top_X contains the percentage of the total counts that is made up by the expression of the top X genes for each cell. The subsets_ columns contain information for the specific gene list that was used. For instance, if a gene list containing mitochondrial genes named mito was used, subsets_mito_sum would contains the total number of mitochondrial counts for each cell.
2.1 Total Counts
2.2 Total Features
2.3 Total Mitochondrial Counts
NULL
2.4 Total Mitochondrial Features
NULL
2.5 % of Library Size Occupied by Top 50 Exp. Genes
2.6 Parameters
| useAssay | counts |
| collectionName | NULL |
| geneSetList | NULL |
| geneSetListLocation | rownames |
| percent_top | 50 100 200 500 |
| use_altexps | FALSE |
| flatten | TRUE |
| detectionLimit | 0 |
| packageVersion | 1.18.1 |
In this function, the inSCE parameter is the input SingleCellExperiment object, while the useAssay parameter is the assay object that in the SingleCellExperiment object the user wishes to use.
3 Doublet Detection
3.1 Scrublet
Scrublet aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset. The wrapper function runScrublet can be used to separately run the Scrublet algorithm on its own. The wrapper function plotScrubletResults can be used to plot the results from the Scrublet algorithm. The Scrublet outputs are scrublet_score, which is a numeric variable of the likelihood that a cell is a doublet, and the scrublet_label, which is the assignment of whether the cell is a doublet.
3.1.1 PBMC_Sample_Name_1
3.1.1.1 Scrublet Doublet Score
3.1.1.2 Density Score
3.1.1.3 Violin Score
3.1.1.4 Scrublet Doublet Assignment
3.1.1.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.2 PBMC_Sample_Name_2
3.1.2.1 Scrublet Doublet Score
3.1.2.2 Density Score
3.1.2.3 Violin Score
3.1.2.4 Scrublet Doublet Assignment
3.1.2.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.3 PBMC_Sample_Name_3
3.1.3.1 Scrublet Doublet Score
3.1.3.2 Density Score
3.1.3.3 Violin Score
3.1.3.4 Scrublet Doublet Assignment
3.1.3.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.4 PBMC_Sample_Name_4
3.1.4.1 Scrublet Doublet Score
3.1.4.2 Density Score
3.1.4.3 Violin Score
3.1.4.4 Scrublet Doublet Assignment
3.1.4.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.5 PBMC_Sample_Name_5
3.1.5.1 Scrublet Doublet Score
3.1.5.2 Density Score
3.1.5.3 Violin Score
3.1.5.4 Scrublet Doublet Assignment
3.1.5.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.6 PBMC_Sample_Name_6
3.1.6.1 Scrublet Doublet Score
3.1.6.2 Density Score
3.1.6.3 Violin Score
3.1.6.4 Scrublet Doublet Assignment
3.1.6.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.7 PBMC_Sample_Name_7
3.1.7.1 Scrublet Doublet Score
3.1.7.2 Density Score
3.1.7.3 Violin Score
3.1.7.4 Scrublet Doublet Assignment
3.1.7.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.8 PBMC_Sample_Name_8
3.1.8.1 Scrublet Doublet Score
3.1.8.2 Density Score
3.1.8.3 Violin Score
3.1.8.4 Scrublet Doublet Assignment
3.1.8.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.9 PBMC_Sample_Name_9
3.1.9.1 Scrublet Doublet Score
3.1.9.2 Density Score
3.1.9.3 Violin Score
3.1.9.4 Scrublet Doublet Assignment
3.1.9.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.1.10 PBMC_Sample_Name_10
3.1.10.1 Scrublet Doublet Score
3.1.10.2 Density Score
3.1.10.3 Violin Score
3.1.10.4 Scrublet Doublet Assignment
3.1.10.5 Parameters
| useAssay | counts |
| simDoubletRatio | 2 |
| nNeighbors | NULL |
| minDist | NULL |
| expectedDoubletRate | 0.1 |
| stdevDoubletRate | 0.02 |
| syntheticDoubletUmiSubsampling | 1 |
| useApproxNeighbors | TRUE |
| distanceMetric | euclidean |
| getDoubletNeighborParents | FALSE |
| minCounts | 3 |
| minCells | 3 |
| minGeneVariabilityPctl | 85 |
| logTransform | FALSE |
| meanCenter | TRUE |
| normalizeVariance | TRUE |
| nPrinComps | 30 |
| tsneAngle | NULL |
| tsnePerplexity | NULL |
| verbose | TRUE |
| seed | 12345 |
Scrublet also has a large set of parameters that the user can adjust; please refer to the Scrublet website for more details.
3.2 DoubletFinder
DoubletFinder is a doublet detection algorithm which depends on the single cell analysis package Seurat. The wrapper function runDoubletFinder can be used to separately run the DoubletFinder algorithm on its own. The wrapper function plotDoubletFinderResults can be used to plot the QC outputs from the DoubletFinder algorithm. The DoubletFinder outputs are doubletFinder_doublet_score, which is a numeric variable of the likelihood that a cell is a doublet, and the doubletFinder_doublet_label, which is the assignment of whether the cell is a doublet.
3.2.1 PBMC_Sample_Name_1
3.2.1.1 Resolution: 1.5
3.2.1.1.1 DoubletFinder Doublet Score
3.2.1.1.2 Density of Doublet Score
3.2.1.1.3 Violin of Doublet Score
3.2.1.1.4 DoubletFinder Doublet Assignment
3.2.1.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.2 PBMC_Sample_Name_2
3.2.2.1 Resolution: 1.5
3.2.2.1.1 DoubletFinder Doublet Score
3.2.2.1.2 Density of Doublet Score
3.2.2.1.3 Violin of Doublet Score
3.2.2.1.4 DoubletFinder Doublet Assignment
3.2.2.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.3 PBMC_Sample_Name_3
3.2.3.1 Resolution: 1.5
3.2.3.1.1 DoubletFinder Doublet Score
3.2.3.1.2 Density of Doublet Score
3.2.3.1.3 Violin of Doublet Score
3.2.3.1.4 DoubletFinder Doublet Assignment
3.2.3.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.4 PBMC_Sample_Name_4
3.2.4.1 Resolution: 1.5
3.2.4.1.1 DoubletFinder Doublet Score
3.2.4.1.2 Density of Doublet Score
3.2.4.1.3 Violin of Doublet Score
3.2.4.1.4 DoubletFinder Doublet Assignment
3.2.4.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.5 PBMC_Sample_Name_5
3.2.5.1 Resolution: 1.5
3.2.5.1.1 DoubletFinder Doublet Score
3.2.5.1.2 Density of Doublet Score
3.2.5.1.3 Violin of Doublet Score
3.2.5.1.4 DoubletFinder Doublet Assignment
3.2.5.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.6 PBMC_Sample_Name_6
3.2.6.1 Resolution: 1.5
3.2.6.1.1 DoubletFinder Doublet Score
3.2.6.1.2 Density of Doublet Score
3.2.6.1.3 Violin of Doublet Score
3.2.6.1.4 DoubletFinder Doublet Assignment
3.2.6.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.7 PBMC_Sample_Name_7
3.2.7.1 Resolution: 1.5
3.2.7.1.1 DoubletFinder Doublet Score
3.2.7.1.2 Density of Doublet Score
3.2.7.1.3 Violin of Doublet Score
3.2.7.1.4 DoubletFinder Doublet Assignment
3.2.7.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.8 PBMC_Sample_Name_8
3.2.8.1 Resolution: 1.5
3.2.8.1.1 DoubletFinder Doublet Score
3.2.8.1.2 Density of Doublet Score
3.2.8.1.3 Violin of Doublet Score
3.2.8.1.4 DoubletFinder Doublet Assignment
3.2.8.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.9 PBMC_Sample_Name_9
3.2.9.1 Resolution: 1.5
3.2.9.1.1 DoubletFinder Doublet Score
3.2.9.1.2 Density of Doublet Score
3.2.9.1.3 Violin of Doublet Score
3.2.9.1.4 DoubletFinder Doublet Assignment
3.2.9.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.2.10 PBMC_Sample_Name_10
3.2.10.1 Resolution: 1.5
3.2.10.1.1 DoubletFinder Doublet Score
3.2.10.1.2 Density of Doublet Score
3.2.10.1.3 Violin of Doublet Score
3.2.10.1.4 DoubletFinder Doublet Assignment
3.2.10.1.5 Parameters
| useAssay | counts |
| seed | 12345 |
| seuratNfeatures | 2000 |
| seuratPcs | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
| seuratRes | 1.5 |
| formationRate | 0.075 |
| nCores | NULL |
| verbose | FALSE |
| packageVersion | 2.0.2 |
runDoubletFinder relies on a parameter (in Seurat) called resolution to determine cells that may be doublets. Users will be able to manipulate the resolution parameter through seuratRes. If multiple numeric vectors are stored in seuratRes, there will be multiple label/scores. The seuratNfeatures parameter determines the number of features that is used in the FindVariableFeatures function in Seurat. seuratPcs parameter determines the number of dimensions used in the FindNeighbors function in Seurat. The formationRate parameter is the estimated doublet detection rate in the dataset. aims to detect doublets by creating simulated doublets from combining transcriptomic profiles of existing cells in the dataset.
3.3 ScDblFinder
scDblFinder is a doublet detection algorithm in the scran package. scDblFinder aims to detect doublets by creating a simulated doublet from existing cells and projecting it to the same PCA space as the cells. The wrapper function runScDblFinder can be used to separately run the scDblFinder algorithm on its own. The wrapper function plotScDblFinderResults can be used to plot the QC outputs from the scDblFinder algorithm. The output of scDblFinder is a scDblFinder_doublet_score and scDblFinder_doublet_call. The doublet score of a droplet will be higher if the it is deemed likely to be a doublet.
3.3.1 PBMC_Sample_Name_1
3.3.1.1 ScDblFinder Doublet Score
3.3.1.2 Density Score
3.3.1.3 Violin Score
3.3.1.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.2 PBMC_Sample_Name_2
3.3.2.1 ScDblFinder Doublet Score
3.3.2.2 Density Score
3.3.2.3 Violin Score
3.3.2.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.3 PBMC_Sample_Name_3
3.3.3.1 ScDblFinder Doublet Score
3.3.3.2 Density Score
3.3.3.3 Violin Score
3.3.3.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.4 PBMC_Sample_Name_4
3.3.4.1 ScDblFinder Doublet Score
3.3.4.2 Density Score
3.3.4.3 Violin Score
3.3.4.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.5 PBMC_Sample_Name_5
3.3.5.1 ScDblFinder Doublet Score
3.3.5.2 Density Score
3.3.5.3 Violin Score
3.3.5.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.6 PBMC_Sample_Name_6
3.3.6.1 ScDblFinder Doublet Score
3.3.6.2 Density Score
3.3.6.3 Violin Score
3.3.6.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.7 PBMC_Sample_Name_7
3.3.7.1 ScDblFinder Doublet Score
3.3.7.2 Density Score
3.3.7.3 Violin Score
3.3.7.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.8 PBMC_Sample_Name_8
3.3.8.1 ScDblFinder Doublet Score
3.3.8.2 Density Score
3.3.8.3 Violin Score
3.3.8.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.9 PBMC_Sample_Name_9
3.3.9.1 ScDblFinder Doublet Score
3.3.9.2 Density Score
3.3.9.3 Violin Score
3.3.9.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.3.10 PBMC_Sample_Name_10
3.3.10.1 ScDblFinder Doublet Score
3.3.10.2 Density Score
3.3.10.3 Violin Score
3.3.10.4 Parameters
| useAssay | counts |
| nNeighbors | 50 |
| simDoublets | 10000 |
| seed | 12345 |
| packageVersion | 1.4.0 |
The nNeighbors parameter is the number of nearest neighbor used to calculate the density for doublet detection. simDoublets is used to determine the number of simulated doublets used for doublet detection.
3.4 Cxds
CXDS, or co-expression based doublet scoring, is an algorithm in the SCDS package which employs a binomial model for the co-expression of pairs of genes to determine doublets. The wrapper function runCxds can be used to separately run the CXDS algorithm on its own. The wrapper function plotCxdsResults can be used to plot the QC outputs from the CXDS algorithm. The output of runCxds is the doublet score, scds_cxds_score.
3.4.1 PBMC_Sample_Name_1
3.4.1.1 Cxds Doublet Score
3.4.1.2 Density Score
3.4.1.3 Violin Score
3.4.1.4 Cxds Doublet Assignment
3.4.1.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.2 PBMC_Sample_Name_2
3.4.2.1 Cxds Doublet Score
3.4.2.2 Density Score
3.4.2.3 Violin Score
3.4.2.4 Cxds Doublet Assignment
3.4.2.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.3 PBMC_Sample_Name_3
3.4.3.1 Cxds Doublet Score
3.4.3.2 Density Score
3.4.3.3 Violin Score
3.4.3.4 Cxds Doublet Assignment
3.4.3.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.4 PBMC_Sample_Name_4
3.4.4.1 Cxds Doublet Score
3.4.4.2 Density Score
3.4.4.3 Violin Score
3.4.4.4 Cxds Doublet Assignment
3.4.4.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.5 PBMC_Sample_Name_5
3.4.5.1 Cxds Doublet Score
3.4.5.2 Density Score
3.4.5.3 Violin Score
3.4.5.4 Cxds Doublet Assignment
3.4.5.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.6 PBMC_Sample_Name_6
3.4.6.1 Cxds Doublet Score
3.4.6.2 Density Score
3.4.6.3 Violin Score
3.4.6.4 Cxds Doublet Assignment
3.4.6.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.7 PBMC_Sample_Name_7
3.4.7.1 Cxds Doublet Score
3.4.7.2 Density Score
3.4.7.3 Violin Score
3.4.7.4 Cxds Doublet Assignment
3.4.7.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.8 PBMC_Sample_Name_8
3.4.8.1 Cxds Doublet Score
3.4.8.2 Density Score
3.4.8.3 Violin Score
3.4.8.4 Cxds Doublet Assignment
3.4.8.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.9 PBMC_Sample_Name_9
3.4.9.1 Cxds Doublet Score
3.4.9.2 Density Score
3.4.9.3 Violin Score
3.4.9.4 Cxds Doublet Assignment
3.4.9.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.4.10 PBMC_Sample_Name_10
3.4.10.1 Cxds Doublet Score
3.4.10.2 Density Score
3.4.10.3 Violin Score
3.4.10.4 Cxds Doublet Assignment
3.4.10.5 Parameters
| seed | 12345 |
| ntop | 500 |
| binThresh | 0 |
| verb | FALSE |
| retRes | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runCxds, the ntop parameter is the number of top variance genes to consider. The binThresh parameter is the minimum counts a gene needs to have to be included in the analysis. verb determines whether progress messages will be displayed or not. retRes will determine whether the gene pair results should be returned or not. The user may set the estimated number of doublets with estNdbl.
3.5 Bcds
BCDS, or binary classification based doublet scoring, is an algorithm in the SCDS package which uses a binary classification approach to determine doublets. The wrapper function runBcds can be used to separately run the BCDS algorithm on its own. The wrapper function plotBCDSResults can be used to plot the QC outputs from the BCDS algorithm. The output of runBcds is scds_bcds_score, which is the likelihood that a cell is a doublet.
3.5.1 PBMC_Sample_Name_1
3.5.1.1 Bcds Doublet Score
3.5.1.2 Density Score
3.5.1.3 Violin Score
3.5.1.4 Bcds Doublet Assignment
3.5.1.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.2 PBMC_Sample_Name_2
3.5.2.1 Bcds Doublet Score
3.5.2.2 Density Score
3.5.2.3 Violin Score
3.5.2.4 Bcds Doublet Assignment
3.5.2.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.3 PBMC_Sample_Name_3
3.5.3.1 Bcds Doublet Score
3.5.3.2 Density Score
3.5.3.3 Violin Score
3.5.3.4 Bcds Doublet Assignment
3.5.3.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.4 PBMC_Sample_Name_4
3.5.4.1 Bcds Doublet Score
3.5.4.2 Density Score
3.5.4.3 Violin Score
3.5.4.4 Bcds Doublet Assignment
3.5.4.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.5 PBMC_Sample_Name_5
3.5.5.1 Bcds Doublet Score
3.5.5.2 Density Score
3.5.5.3 Violin Score
3.5.5.4 Bcds Doublet Assignment
3.5.5.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.6 PBMC_Sample_Name_6
3.5.6.1 Bcds Doublet Score
3.5.6.2 Density Score
3.5.6.3 Violin Score
3.5.6.4 Bcds Doublet Assignment
3.5.6.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.7 PBMC_Sample_Name_7
3.5.7.1 Bcds Doublet Score
3.5.7.2 Density Score
3.5.7.3 Violin Score
3.5.7.4 Bcds Doublet Assignment
3.5.7.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.8 PBMC_Sample_Name_8
3.5.8.1 Bcds Doublet Score
3.5.8.2 Density Score
3.5.8.3 Violin Score
3.5.8.4 Bcds Doublet Assignment
3.5.8.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.9 PBMC_Sample_Name_9
3.5.9.1 Bcds Doublet Score
3.5.9.2 Density Score
3.5.9.3 Violin Score
3.5.9.4 Bcds Doublet Assignment
3.5.9.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.5.10 PBMC_Sample_Name_10
3.5.10.1 Bcds Doublet Score
3.5.10.2 Density Score
3.5.10.3 Violin Score
3.5.10.4 Bcds Doublet Assignment
3.5.10.5 Parameters
| seed | 12345 |
| ntop | 500 |
| srat | 1 |
| verb | FALSE |
| retRes | FALSE |
| nmax | tune |
| varImp | FALSE |
| estNdbl | TRUE |
| useAssay | counts |
| packageVersion | 1.6.0 |
In runBcds, the ntop parameter is the number of top variance genes to consider. The srat parameter is the ratio between original number of cells and simulated doublets. The nmax parameter is the maximum number of cycles that the algorithm should run through. If set to tune, this will be automatic. The varImp parameter determines if the variable importance should be returned or not.
3.6 ScdsHybrid
The CXDS-BCDS hybrid algorithm, uses both CXDS and BCDS algorithms from the SCDS package. The wrapper function runCxdsBcdsHybrid can be used to separately run the CXDS-BCDS hybrid algorithm on its own. The wrapper function plotScdsHybridResults can be used to plot the QC outputs from the CXDS-BCDS hybrid algorithm. The output of runCxdsBcdsHybrid is the doublet score, scds_hybrid_score.
3.6.1 PBMC_Sample_Name_1
3.6.1.1 ScdsHybrid Doublet Score
3.6.1.2 Density Score
3.6.1.3 Violin Score
3.6.1.4 ScdsHybrid Doublet Assignment
3.6.1.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.2 PBMC_Sample_Name_2
3.6.2.1 ScdsHybrid Doublet Score
3.6.2.2 Density Score
3.6.2.3 Violin Score
3.6.2.4 ScdsHybrid Doublet Assignment
3.6.2.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.3 PBMC_Sample_Name_3
3.6.3.1 ScdsHybrid Doublet Score
3.6.3.2 Density Score
3.6.3.3 Violin Score
3.6.3.4 ScdsHybrid Doublet Assignment
3.6.3.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.4 PBMC_Sample_Name_4
3.6.4.1 ScdsHybrid Doublet Score
3.6.4.2 Density Score
3.6.4.3 Violin Score
3.6.4.4 ScdsHybrid Doublet Assignment
3.6.4.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.5 PBMC_Sample_Name_5
3.6.5.1 ScdsHybrid Doublet Score
3.6.5.2 Density Score
3.6.5.3 Violin Score
3.6.5.4 ScdsHybrid Doublet Assignment
3.6.5.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.6 PBMC_Sample_Name_6
3.6.6.1 ScdsHybrid Doublet Score
3.6.6.2 Density Score
3.6.6.3 Violin Score
3.6.6.4 ScdsHybrid Doublet Assignment
3.6.6.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.7 PBMC_Sample_Name_7
3.6.7.1 ScdsHybrid Doublet Score
3.6.7.2 Density Score
3.6.7.3 Violin Score
3.6.7.4 ScdsHybrid Doublet Assignment
3.6.7.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.8 PBMC_Sample_Name_8
3.6.8.1 ScdsHybrid Doublet Score
3.6.8.2 Density Score
3.6.8.3 Violin Score
3.6.8.4 ScdsHybrid Doublet Assignment
3.6.8.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.9 PBMC_Sample_Name_9
3.6.9.1 ScdsHybrid Doublet Score
3.6.9.2 Density Score
3.6.9.3 Violin Score
3.6.9.4 ScdsHybrid Doublet Assignment
3.6.9.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
3.6.10 PBMC_Sample_Name_10
3.6.10.1 ScdsHybrid Doublet Score
3.6.10.2 Density Score
3.6.10.3 Violin Score
3.6.10.4 ScdsHybrid Doublet Assignment
3.6.10.5 Parameters
| seed | 12345 |
| nTop | 500 |
| cxdsArgs | NULL |
| bcdsArgs | NULL |
| verb | FALSE |
| estNdbl | TRUE |
| force | FALSE |
| useAssay | counts |
| packageVersion | 1.6.0 |
All parameters from the runBCDS and runBCDS functions may be applied to this function in the cxdsArgs and bcdsArgs parameters, respectively.
4 Ambient RNA Detection
4.1 DecontX
In droplet-based single cell technologies, ambient RNA that may have been released from apoptotic or damaged cells may get incorporated into another droplet, and can lead to contamination. decontX, available from the celda, is a Bayesian method for the identification of the contamination level at a cellular level. The wrapper function runDecontX can be used to separately run the DecontX algorithm on its own. The wrapper function plotDecontXResults can be used to plot the QC outputs from the DecontX algorithm. The outputs of runDecontX are decontX_contamination and decontX_clusters. decontX_contamination is a numeric vector which characterizes the level of contamination in each cell. Clustering is performed as part of the runDecontX algorithm. decontX_clusters is the resulting cluster assignment, which can also be labeled on the plot.
4.1.1 PBMC_Sample_Name_1
4.1.1.1 DecontX Contamination Score
4.1.1.2 Density Score
4.1.1.3 Violin Score
4.1.1.4 DecontX Clusters
4.1.1.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.2 PBMC_Sample_Name_2
4.1.2.1 DecontX Contamination Score
4.1.2.2 Density Score
4.1.2.3 Violin Score
4.1.2.4 DecontX Clusters
4.1.2.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.3 PBMC_Sample_Name_3
4.1.3.1 DecontX Contamination Score
4.1.3.2 Density Score
4.1.3.3 Violin Score
4.1.3.4 DecontX Clusters
4.1.3.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.4 PBMC_Sample_Name_4
4.1.4.1 DecontX Contamination Score
4.1.4.2 Density Score
4.1.4.3 Violin Score
4.1.4.4 DecontX Clusters
4.1.4.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.5 PBMC_Sample_Name_5
4.1.5.1 DecontX Contamination Score
4.1.5.2 Density Score
4.1.5.3 Violin Score
4.1.5.4 DecontX Clusters
4.1.5.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.6 PBMC_Sample_Name_6
4.1.6.1 DecontX Contamination Score
4.1.6.2 Density Score
4.1.6.3 Violin Score
4.1.6.4 DecontX Clusters
4.1.6.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.7 PBMC_Sample_Name_7
4.1.7.1 DecontX Contamination Score
4.1.7.2 Density Score
4.1.7.3 Violin Score
4.1.7.4 DecontX Clusters
4.1.7.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.8 PBMC_Sample_Name_8
4.1.8.1 DecontX Contamination Score
4.1.8.2 Density Score
4.1.8.3 Violin Score
4.1.8.4 DecontX Clusters
4.1.8.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.9 PBMC_Sample_Name_9
4.1.9.1 DecontX Contamination Score
4.1.9.2 Density Score
4.1.9.3 Violin Score
4.1.9.4 DecontX Clusters
4.1.9.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
4.1.10 PBMC_Sample_Name_10
4.1.10.1 DecontX Contamination Score
4.1.10.2 Density Score
4.1.10.3 Violin Score
4.1.10.4 DecontX Clusters
4.1.10.5 Parameters
| useAssay | counts |
| z | NULL |
| maxIter | 500 |
| delta | 10 10 |
| estimateDelta | TRUE |
| convergence | 0.001 |
| iterLogLik | 10 |
| varGenes | 5000 |
| dbscanEps | 1 |
| seed | 12345 |
| logfile | NULL |
| verbose | TRUE |
| packageVersion | 1.7.7 |
5 Session Info
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS: /share/pkg.7/r/4.0.2/install/lib/libopenblasp-r0.3.7.so
## LAPACK: /share/pkg.7/r/4.0.2/install/lib/liblapack.so.3.9.0
##
## locale:
## [1] C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] dplyr_1.0.2 ggplot2_3.3.2
## [3] singleCellTK_2.0.1 DelayedArray_0.16.0
## [5] Matrix_1.2-18 SingleCellExperiment_1.12.0
## [7] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
## [11] IRanges_2.24.0 S4Vectors_0.28.0
## [13] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
## [15] matrixStats_0.57.0 devtools_2.3.2
## [17] usethis_1.6.3
##
## loaded via a namespace (and not attached):
## [1] softImpute_1.4 pbapply_1.4-3
## [3] lattice_0.20-41 GSVA_1.38.0
## [5] vctrs_0.3.5 mgcv_1.8-31
## [7] GSVAdata_1.26.0 blob_1.2.1
## [9] survival_3.1-12 spatstat.data_2.0-0
## [11] later_1.1.0.1 DBI_1.1.0
## [13] R.utils_2.10.1 rappdirs_0.3.1
## [15] uwot_0.1.9 xopen_1.0.0
## [17] dqrng_0.2.1 jpeg_0.1-8.1
## [19] enrichR_2.1 zlibbioc_1.36.0
## [21] htmlwidgets_1.5.2 mvtnorm_1.1-1
## [23] GlobalOptions_0.1.2 future_1.20.1
## [25] leiden_0.3.5 multipanelfigure_2.1.2
## [27] scater_1.18.3 irlba_2.3.3
## [29] Rcpp_1.0.6 KernSmooth_2.23-16
## [31] DT_0.16 promises_1.1.1
## [33] limma_3.46.0 dbscan_1.1-5
## [35] pkgload_1.1.0 magick_2.5.0
## [37] graph_1.68.0 Hmisc_4.4-1
## [39] startupmsg_0.9.6 RSpectra_0.16-0
## [41] fs_1.5.0 assertive.types_0.0-3
## [43] mnormt_2.0.2 digest_0.6.27
## [45] png_0.1-7 bluster_1.0.0
## [47] sctransform_0.3.2 cowplot_1.1.0
## [49] pkgconfig_2.0.3 DelayedMatrixStats_1.12.0
## [51] ggbeeswarm_0.6.0 iterators_1.0.13
## [53] DropletUtils_1.10.0 reticulate_1.18
## [55] circlize_0.4.11 spam_2.5-1
## [57] beeswarm_0.2.3 GetoptLong_1.0.4
## [59] xfun_0.19 zoo_1.8-8
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## [65] rcmdcheck_1.3.3 viridisLite_0.3.0
## [67] rtracklayer_1.50.0 pkgbuild_1.1.0
## [69] rlang_0.4.8 glue_1.4.2
## [71] metap_1.4 ensembldb_2.14.0
## [73] RColorBrewer_1.1-2 stringr_1.4.0
## [75] sva_3.38.0 fields_11.6
## [77] DESeq2_1.30.0 kableExtra_1.3.1
## [79] labeling_0.4.2 mutoss_0.1-12
## [81] gbRd_0.4-11 M3Drop_1.16.0
## [83] httpuv_1.5.4 assertive.base_0.0-7
## [85] TH.data_1.0-10 BiocNeighbors_1.8.1
## [87] webshot_0.5.2 annotate_1.68.0
## [89] MCMCprecision_0.4.0 jsonlite_1.7.1
## [91] XVector_0.30.0 tmvnsim_1.0-2
## [93] bit_4.0.4 shinyFiles_0.9.0
## [95] mime_0.9 gridExtra_2.3
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## [99] assertive.properties_0.0-4 stringi_1.5.3
## [101] processx_3.4.4 distr_2.8.0
## [103] spatstat.sparse_1.2-1 rbibutils_1.4
## [105] scattermore_0.7 Rdpack_2.1
## [107] bitops_1.0-6 cli_2.1.0
## [109] rhdf5filters_1.2.0 maps_3.3.0
## [111] batchelor_1.6.0 RSQLite_2.2.1
## [113] tidyr_1.1.2 data.table_1.13.2
## [115] ruv_0.9.7.1 rstudioapi_0.13
## [117] GenomicAlignments_1.26.0 sfsmisc_1.1-7
## [119] nlme_3.1-148 scran_1.18.1
## [121] locfit_1.5-9.4 scDblFinder_1.4.0
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## [127] dbplyr_2.0.0 sessioninfo_1.1.1
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## [131] munsell_0.5.0 R.methodsS3_1.8.1
## [133] caTools_1.18.0 codetools_0.2-16
## [135] vipor_0.4.5 lmtest_0.9-38
## [137] shinyWidgets_0.5.4 msigdbr_7.2.1
## [139] htmlTable_2.1.0 assertive.files_0.0-2
## [141] xtable_1.8-4 ROCR_1.0-11
## [143] BiocManager_1.30.10 abind_1.4-5
## [145] farver_2.0.3 FNN_1.1.3
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## [149] AnnotationHub_2.22.0 RANN_2.6.1
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## [159] shinythemes_1.1.2 tibble_3.0.4
## [161] cluster_2.1.0 future.apply_1.6.0
## [163] Seurat_4.0.1 tidytree_0.3.3
## [165] ellipsis_0.3.1 prettyunits_1.1.1
## [167] shinyBS_0.61 ggridges_0.5.2
## [169] shinyalert_2.0.0 igraph_1.2.6
## [171] multtest_2.46.0 RcppEigen_0.3.3.7.0
## [173] shinyjs_2.0.0 remotes_2.2.0
## [175] TFisher_0.2.0 scMerge_1.6.0
## [177] testthat_3.0.0 spatstat.utils_2.0-0
## [179] htmltools_0.5.1.1 BiocFileCache_1.14.0
## [181] yaml_2.2.1 GenomicFeatures_1.42.1
## [183] scRNAseq_2.4.0 plotly_4.9.2.1
## [185] interactiveDisplayBase_1.28.0 XML_3.99-0.5
## [187] foreign_0.8-78 withr_2.3.0
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## [235] AnnotationFilter_1.14.0 globals_0.13.1
## [237] R6_2.5.0 Rhdf5lib_1.12.0
## [239] nnet_7.3-13 progress_1.2.2
## [241] tximport_1.18.0 genefilter_1.72.0
## [243] treeio_1.14.1 gtools_3.8.2
## [245] shape_1.4.5 statmod_1.4.35
## [247] beachmat_2.6.1 BiocVersion_3.12.0
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